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公开(公告)号：US11227161B1
公开(公告)日：2022-01-18
申请号：US17471485
申请日：2021-09-10
Inventor： Jianhua Tao , Yu He , Bin Liu , Licai Sun
Abstract: A physiological signal prediction method includes: collecting a video file, the video file containing long-term videos, and contents of the video file containing data for a face of a single person and true physiological signal data; segmenting a single long-term video into multiple short-term video clips; extracting, by using each frame of image in each of the short-term video clips, features of interested regions for identifying physiological signals so as to form features of interested regions of a single frame; splicing, for each of the short-term video clips, features of interested regions of all fixed frames corresponding to the short-term video clip into features of interested regions of a multi-frame video, and converting the features of the interested regions of the multi-frame video into a spatio-temporal graph; inputting the spatio-temporal graph into a deep learning model for training, and using the trained deep learning model to predict physiological signal parameters.
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2.发明授权公开(公告)号：US11238289B1
公开(公告)日：2022-02-01
申请号：US17389364
申请日：2021-07-30
Inventor： Jianhua Tao , Zheng Lian , Bin Liu , Licai Sun
IPC: G06K9/00 , G06N3/04 , G06F16/783 , A61B5/16
Abstract: An automatic lie detection method and apparatus for interactive scenarios, a device and a medium to improve the accuracy of automatic lie detection are provided. The method includes: segmenting three modalities, namely a video, an audio and a text, of a to-be-detected sample; extracting short-term features of the three modalities; integrating the short-term features of the three modalities in the to-be-detected sample to obtain long-term features of the three modalities corresponding to each dialogue; integrating the long-term features of the three modalities by a self-attention mechanism to obtain a multi-modal feature of the each dialogue; integrating the multi-modal feature of the each dialogue with interactive information by a graph neutral network to obtain a multi-modal feature integrated with the interactive information; and predicting a lie level of the each dialogue according to the multi-modal feature integrated with the interactive information.
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公开(公告)号：US11244119B1
公开(公告)日：2022-02-08
申请号：US17389383
申请日：2021-07-30
Inventor： Jianhua Tao , Licai Sun , Bin Liu , Zheng Lian
Abstract: A multi-modal lie detection method and apparatus, and a device to improve an accuracy of an automatic lie detection are provided. The multi-modal lie detection method includes inputting original data of three modalities, namely a to-be-detected audio, a to-be-detected video and a to-be-detected text; performing a feature extraction on input contents to obtain deep features of the three modalities; explicitly depicting first-order, second-order and third-order interactive relationships of the deep features of the three modalities to obtain an integrated multi-modal feature of each word; performing a context modeling on the integrated multi-modal feature of the each word to obtain a final feature of the each word; and pooling the final feature of the each word to obtain global features, and then obtaining a lie classification result by a fully-connected layer.
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公开(公告)号：US11281945B1
公开(公告)日：2022-03-22
申请号：US17468994
申请日：2021-09-08
Inventor： Jianhua Tao , Licai Sun , Bin Liu , Zheng Lian
Abstract: A multimodal dimensional emotion recognition method includes: acquiring a frame-level audio feature, a frame-level video feature, and a frame-level text feature from an audio, a video, and a corresponding text of a sample to be tested; performing temporal contextual modeling on the frame-level audio feature, the frame-level video feature, and the frame-level text feature respectively by using a temporal convolutional network to obtain a contextual audio feature, a contextual video feature, and a contextual text feature; performing weighted fusion on these three features by using a gated attention mechanism to obtain a multimodal feature; splicing the multimodal feature and these three features together to obtain a spliced feature, and then performing further temporal contextual modeling on the spliced feature by using a temporal convolutional network to obtain a contextual spliced feature; and performing regression prediction on the contextual spliced feature to obtain a final dimensional emotion prediction result.
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